Progress toward Quantitative Design Principles of Multicellular Systems

Olimpio, Eduardo P.; Alvarez, Desirée; Youk, Hyun

doi:10.1002/9783527696130.ch13

Cited by 3 publications

(9 citation statements)

References 81 publications

(143 reference statements)

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“…These bidirectional feedbacks are often nonlinear in nature, making their analyses difficult. Multicellular systems frequently exhibit these backand-forth behaviors [42][43][44][45]. For example, cells can communicate with one another by secreting molecules that diffuse from one cell to another.…”

Section: Predictive Landscapes For Cellular Automata That Self-organi...mentioning

confidence: 99%

Predictive landscapes hidden beneath biological cellular automata

Koopmans

Youk

2021

J Biol Phys

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To celebrate Hans Frauenfelder’s achievements, we examine energy(-like) “landscapes” for complex living systems. Energy landscapes summarize all possible dynamics of some physical systems. Energy(-like) landscapes can explain some biomolecular processes, including gene expression and, as Frauenfelder showed, protein folding. But energy-like landscapes and existing frameworks like statistical mechanics seem impractical for describing many living systems. Difficulties stem from living systems being high dimensional, nonlinear, and governed by many, tightly coupled constituents that are noisy. The predominant modeling approach is devising differential equations that are tailored to each living system. This ad hoc approach faces the notorious “parameter problem”: models have numerous nonlinear, mathematical functions with unknown parameter values, even for describing just a few intracellular processes. One cannot measure many intracellular parameters or can only measure them as snapshots in time. Another modeling approach uses cellular automata to represent living systems as discrete dynamical systems with binary variables. Quantitative (Hamiltonian-based) rules can dictate cellular automata (e.g., Cellular Potts Model). But numerous biological features, in current practice, are qualitatively described rather than quantitatively (e.g., gene is (highly) expressed or not (highly) expressed). Cellular automata governed by verbal rules are useful representations for living systems and can mitigate the parameter problem. However, they can yield complex dynamics that are difficult to understand because the automata-governing rules are not quantitative and much of the existing mathematical tools and theorems apply to continuous but not discrete dynamical systems. Recent studies found ways to overcome this challenge. These studies either discovered or suggest an existence of predictive “landscapes” whose shapes are described by Lyapunov functions and yield “equations of motion” for a “pseudo-particle.” The pseudo-particle represents the entire cellular lattice and moves on the landscape, thereby giving a low-dimensional representation of the cellular automata dynamics. We outline this promising modeling strategy.

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Section: Predictive Landscapes For Cellular Automata That Self-organi...mentioning

confidence: 99%

Predictive landscapes hidden beneath biological cellular automata

Koopmans

Youk

2021

J Biol Phys

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“…As such, the evolution of complex signaling systems increases with complexity of the organisms, from yeasts, to nematode worms, fruit flies and humans [3]. A cell that secretes and senses the same molecule is said to be communicating with itself and this is referred to as "selfcommunication" [1,4,5]. Conversely, a cell that communicates with its neighbouring cells and not with itself undergoes "neighbour communication" [1,5].…”

Section: Introductionmentioning

confidence: 99%

“…A cell that secretes and senses the same molecule is said to be communicating with itself and this is referred to as "selfcommunication" [1,4,5]. Conversely, a cell that communicates with its neighbouring cells and not with itself undergoes "neighbour communication" [1,5]. However, the secreting and sensing cell may communicate with both itself and with its neighbours [4].…”

Section: Introductionmentioning

confidence: 99%

“…One common mechanism for detecting and transmitting extracellular signals uses cell surface receptors coupled to intracellular heterotrimeric guanine nucleotide-binding proteins (G proteins) [13,20]. It is important to note that although usually stated as separate entities, extracellular and intracellular sensing may be interdependent [5,21]. Conrad et al stated that the glucose repression pathway in the yeast Saccharomyces cerevisiae raised a lot of interest due to its involvement in various cascades of nitrogen catabolite repression, general amino acid control (GAAC), phosphate regulation, and regulation controlling ethanol fermentation.…”

Section: Introductionmentioning

confidence: 99%

“…A combination of genetic, biochemical and molecular biological analysis of the response of haploid yeast cells to their peptide mating pheromones has established basic principles of G protein signaling and regulation [13,34]. Budding yeast cells can thus communicate by releasing a signaling molecule called the mating factor [3,5,13]. Other intracellular processes may include: protein synthesis, mitochondrial biogenesis, retrograde response to mitochondrial dysfunction, proteasome machinery, and even programmed cell death.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cell signaling in yeast: A mini review

et al. 2019

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Background: Understanding cellular mechanism of communication is the main goal of systems biology. Unicellular yeasts are effective model to understand the molecular interactions that generate cell polarity induced by external inputs. The mechanisms of many extracellular stimuli are induced by complexes of cell surface receptors, G proteins. The mechanisms of many extracellular stimuli are induced by complexes of cell surface receptors, G proteins and mitogen activated protein (MAP) kinase complexes. Many components, their interrelationships, and their regulators of these mechanisms were initially identified in yeast. A complex web of sensing mechanisms and cooperation among signaling networks such as a cyclic adenosine monophosphate dependent protein kinase, mitogen-activated protein kinase cascade and 5-adenosine monophosphate activated protein kinase induce various changes in physiology, cell polarity, cell cycle progression and gene expression to achieve differentiation. Ras-cAMP pathway explained in yeast model with signalling function of the oncogenic mammalian Ras protein. So studies on yeast cells may enlighten some underlying mechanism which will be beneficial to understand the mechanisms of disease.

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Complex microbial systems across different levels of description

Bronk

Götz²,

Opitz³

2018

Phys. Biol.

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Complex biological systems offer a variety of interesting phenomena at the different physical scales. With increasing abstraction, details of the microscopic scales can often be extrapolated to average or typical macroscopic properties. However, emergent properties and cross-scale interactions can impede naïve abstractions and necessitate comprehensive investigations of these complex systems. In this review paper, we focus on microbial communities, and first, summarize a general hierarchy of relevant scales and description levels to understand these complex systems: (1) genetic networks, (2) single cells, (3) populations, and (4) emergent multi-cellular properties. Second, we employ two illustrating examples, microbial competition and biofilm formation, to elucidate how cross-scale interactions and emergent properties enrich the observed multi-cellular behavior in these systems. Finally, we conclude with pointing out the necessity of multi-scale investigations to understand complex biological systems and discuss recent investigations.

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Progress toward Quantitative Design Principles of Multicellular Systems

Cited by 3 publications

References 81 publications

Predictive landscapes hidden beneath biological cellular automata

Predictive landscapes hidden beneath biological cellular automata

Cell signaling in yeast: A mini review

Complex microbial systems across different levels of description

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