Systems Biology

Dynamic signaling networks

We are interested in computational and mathematical models based on the theory of computer science and dynamical systems to describe, simulate, and predict the behavior of complex biological processes. We study dynamic models and perform in-silico experiments involving form, shape, and patterning formation, signaling mechanisms responsible for cell behaviors, and synthetic regulatory networks engineered to perform specific functions. These models aid us to predict the outcomes of novel perturbations, understand why these regulatory mechanisms can go awry such as in cancer, and, more importantly, find interventions that can repair and restore their original functionality.

Dynamic system

Pattern, shape, and form regulation

The regulation of the formation of biological patterns, shapes, and forms has been historically one of the most difficult aspects to understand in biology. The complexity of biological regulation together with the multi-dimensional spatial characteristics of these processes represent a hard barrier for our comprehension. We seek to discover mechanistic models to aid us to understand, predict, and control the formation of biological spatial properties. We use dynamic models at the organism and tissue levels to simulate patterns, shapes, and forms together with the signaling mechanisms controlling their formation. These spatial dynamic models are already paving the way for the long sought comprehensive theory of growth and form.

Model of planarian regeneration

Stochastic outcomes

Interestingly, most biological experiments result in stochastic outcomes. Knocking down certain genes or applying a pharmacological drug to genetically-clone animals usually produce a set of different results, each of them with a specific probability. Is this stochasticity due to the noise of biological interactions, some unknown variability within the organisms, or an inherent property of the signaling mechanisms? We investigate stochastic dynamic models to discover the source of this variability, study their dynamic properties, such as their space state and dynamic attractors, and how specific pharmacological and genetic interventions can create bifurcations in the system. Importantly, these analyses can reveal possible treatments to revert undesirable conditions characteristic of certain diseases.

Stochastic outcomes

Publications

Discovering novel phenotypes with automatically inferred dynamic models: a partial melanocyte conversion in Xenopus
D. Lobo, M. Lobikin, M. Levin
Scientific Reports 7, 41339, 2017.


Modeling regenerative processes with Membrane Computing
M. García-Quismondo, M. Levin, D. Lobo
Information Sciences 381, pp. 229-249, 2017.


Computational discovery and in vivo validation of hnf4 as a regulatory gene in planarian regeneration
D. Lobo, J. Morokuma, M. Levin
Bioinformatics 32(17), pp. 2681-2685, 2016.


Computing a worm: reverse-engineering planarian regeneration
D. Lobo, M. Levin
Advances in Unconventional Computing
A. Adamatzky (ed.)
Springer 637-654, 2016.


Physiological controls of large-scale patterning in planarian regeneration: a molecular and computational perspective on growth and form
F. Durant, D. Lobo, J. Hammelman, M. Levin
Regeneration 3(2), pp. 78-102, 2016.
(Selected for the journal cover)


MoCha: molecular characterization of unknown pathways
D. Lobo, J. Hammelman, M. Levin
Journal of Computational Biology 23(4): 291-297, 2016.


A dynamic architecture of life
B.P. Rubin, J. Brockes, B. Galliot, U. Grossniklaus, D. Lobo, M. Mainardi, M. Mirouze, A. Prochiantz, A. Steger
F1000Research 4:1288, 2015.


Serotonergic regulation of melanocyte conversion: A bioelectrically regulated network for stochastic all-or-none hyperpigmentation
M. Lobikin, D. Lobo, D.J. Blackiston, C.J. Martyniuk, E. Tkachenko, M. Levin
Science Signaling 8(397), pp. ra99, 2015.
(Reviewed in a focus paper)


Inferring regulatory networks from experimental morphological phenotypes: a computational method reverse-engineers planarian regeneration
D. Lobo, M. Levin
PLoS Computational Biology 11(6): e1004295, 2015.


A linear-encoding model explains the variability of the target morphology in regeneration
D. Lobo, M. Solano, G.A. Bubenik, M. Levin
Journal of the Royal Society Interface 30(24), pp. 3598-3600, 2014.
(Recommended by F1000Prime, Faculty of 1000, 718232471)


Resting potential, oncogene-induced tumorigenesis, and metastasis: the bioelectric basis of cancer in vivo
M. Lobikin, B. Chernet, D. Lobo, M. Levin
Physical Biology 9(6): 065002, 2012.
(Selected for the journal cover)


Modeling planarian regeneration: a primer for reverse-engineering the worm
D. Lobo, W.S. Beane, M. Levin
PLoS Computational Biology 8(4): e1002481, 2012.
(Selected for the journal cover)