Inferring Tumor Proliferative Organization from Phylogenetic Tree Measures in a Computational Model

Scott, Jacob G.; Maini, Philip K.; Anderson, Alexander R.A.; Fletcher, Alexander G.

doi:10.1093/sysbio/syz070

Cited by 17 publications

(15 citation statements)

References 79 publications

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“…Phylogenetic trees are a useful tool to infer evolutionary processes of genetic diversification and selection. Moreover, the topology of the phylogenetic tree has been used as a quantitative measure of the underlying evolutionary processes they result from (Colijn and Plazzotta, 2018;Dayarian and Shraiman, 2014;Manceau et al, 2015;Neher et al, 2014;Scott et al, 2019).…”

Section: Topological Features Of Simulated Phylogenies Delineate Cin mentioning

confidence: 99%

Quantifying chromosomal instability from intratumoral karyotype diversity using agent-based modeling and Bayesian inference

Lynch

Arp

Zhou

et al. 2021

Preprint

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Chromosomal instability (CIN) — persistent chromosome gain or loss through abnormal karyokinesis — is a hallmark of cancer that drives aneuploidy. Intrinsic chromosome mis-segregation rates, a measure of CIN, can inform prognosis and are a likely biomarker for response to anti-microtubule agents. However, existing methodologies to measure this rate are labor intensive, indirect, and confounded by karyotype selection reducing observable diversity. We developed a framework to simulate and measure CIN, accounting for karyotype selection, and recapitulated karyotype-level clonality in simulated populations. We leveraged approximate Bayesian computation using phylogenetic topology and diversity to infer mis-segregation rates and karyotype selection from single-cell DNA sequencing data. Experimental validation of this approach revealed extensive chromosome mis-segregation rates caused by the chemotherapy paclitaxel (17.5±0.14/division). Extending this approach to clinical samples revealed the inferred rates fell within direct observations of cancer cell lines. This work provides the necessary framework to quantify CIN in human tumors and develop it as a predictive biomarker.

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Section: Topological Features Of Simulated Phylogenies Delineate Cin mentioning

confidence: 99%

Quantifying chromosomal instability from intratumoral karyotype diversity using agent-based modeling and Bayesian inference

Lynch

Arp

Zhou

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…As a result, computational models of cancer need to account for many of these factors considering the heterogeneity and interactions of single cells, yet contain sufficient numbers of them to predict emergent phenomena at a tumor scale ( Metzcar et al, 2019 ). Using this approach, multi-agent models have been used to help understand metastasis ( Waclaw et al, 2015 ) and show that cancer cells with stem cell-like properties can be a key determinant in cancer progression with fatal consequences ( Scott et al, 2016 , 2019 ).…”

Section: Collective Phenomena Driving Diseasementioning

confidence: 99%

Toward Engineering Biosystems With Emergent Collective Functions

Gorochowski

Hauert

Kreft

et al. 2020

Front. Bioeng. Biotechnol.

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Many complex behaviors in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans many length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modeling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modeling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviors offers a means to take synthetic biology beyond single molecules or cells and toward the creation of systems with functions that can only emerge from collectives at multiple scales.

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“…As a result, computational models of cancer need to account for many of these factors considering the heterogeneity and interactions of single cells, yet contain sufficient numbers of them to predict emergent phenomena at a tumour scale (Metzcar et al, 2019). Using this approach, multi-agent models have been used to help understand metastasis (Waclaw et al, 2015) and shown that cancer cells with stem cell-like properties can be a key determinant in cancer progression with fatal consequences (Scott et al, 2016(Scott et al, , 2019.…”

Section: Collective Phenomena Driving Diseasementioning

confidence: 99%

Towards Engineering Biosystems with Emergent Collective Functions

Gorochowski¹,

Hauert²,

Kreft³

et al. 2020

Preprint

View full text Add to dashboard Cite

Many complex behaviours in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans multiple length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modelling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modelling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviours offers a means to take synthetic biology beyond single molecules or cells and towards the creation of systems with functions that can only emerge from collectives at multiple scales.

show abstract

Inferring Tumor Proliferative Organization from Phylogenetic Tree Measures in a Computational Model

Cited by 17 publications

References 79 publications

Quantifying chromosomal instability from intratumoral karyotype diversity using agent-based modeling and Bayesian inference

Quantifying chromosomal instability from intratumoral karyotype diversity using agent-based modeling and Bayesian inference

Toward Engineering Biosystems With Emergent Collective Functions

Towards Engineering Biosystems with Emergent Collective Functions

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